Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time
Abstract
:1. Introduction
- Development of a medical system for home environments that incorporates IoT and deep learning technologies to monitor health conditions such as arrhythmia, fever, heart rate, and oxygen levels.
- Apply a deep learning model based on CNN with attention layers to classify potential heart issues, including five types of arrhythmias: normal beat, supraventricular premature beat, premature ventricular contraction, fusion of ventricular, unclassifiable beat.
- Demonstration of the high accuracy and performance of the proposed deep learning model in identifying heart conditions achieving an accuracy of 0.982.
- Investigating the potential of integrating IoT and deep learning technologies in medical systems for home environments to provide real-time monitoring, timely intervention, and improved patient care while reducing healthcare costs and hospital visits.
2. Related Work
3. Methods and Materials
3.1. IoT Devices and Sensors
3.2. Data Transmission and Analysis
3.3. Deep Learning Model
3.3.1. Convolutional Layers
3.3.2. Attention Layer
3.3.3. Fully Connected Layers
3.3.4. Output Layer
3.4. Proposed IoT and Deep Learning-Based Framework
4. Experimental Results
4.1. Data Collection and Preprocessing
4.2. Evaluation Metrices
4.3. Experimental Setup and Hyperparameter Optimization
4.4. Results Analysis
Ref. | Year | Methodology | Performance |
---|---|---|---|
Achariya et al. [45] | 2017 | Deep CNN | Acc = 0.945 F1_score = 0.715 |
Plawiak et al. [42] | 2018 | SVM based Neural System | Acc = 0.910 F1_score = 0.894 |
Yıldırım et al. [41] | 2018 | Deep CNN | Acc = 0.913 F1_score = 0.851 |
Chen et al. [40] | 2020 | CNN+LSTM | Acc = 0.992 F1_score = 0.908 |
Yao et al. [46] | 2020 | ATI-CNN | Acc = 0.812 |
Zhou and Tan [47] | 2020 | CNN | Acc = 0.975 |
Hammad et al. [43] | 2020 | DNN + GA + KNN | Acc = 0.980 F1_score = 0.959 |
Kim et al. [44] | 2022 | ResNet + biLSTM | Acc = 0.992 F1_score = 0.916 |
Mohebbian et al. [32] | 2023 | Semi-supervised active transfer learning | Acc = 0.980 |
Proposed method | 2023 | CNN with attention layers | Acc = 0.982 F1_score = 0.980 |
5. Discussion and Future Research
- Integration of more sensors and data sources: While our proposed framework utilizes multiple sensors for data collection and analysis, many other sensors and data sources can be integrated to provide a more comprehensive picture of an individual’s health status. For example, integrating sensors that measure blood pressure, glucose levels, and respiration rates can provide additional insights into an individual’s health.
- Development of personalized health monitoring systems: The proposed framework is designed to monitor the health of individuals in a general sense. However, personalized health monitoring systems can be developed by using deep learning techniques to analyze an individual’s historical health data and provide customized recommendations for improving their health.
- Enhancing the security and privacy of health data: As more health data is collected and transmitted through IoT devices, ensuring the security and privacy of this data becomes increasingly important. Future research can focus on developing robust security and privacy mechanisms to protect health data from unauthorized access and breaches.
- Integration with telemedicine services: The proposed framework can be further enhanced by integrating it with telemedicine services. This would enable healthcare providers to monitor and diagnose patients remotely, reducing the need for in-person visits and improving access to healthcare in rural and remote areas.
- Evaluation of the impact of home environment health monitoring on healthcare outcomes: While home environment health monitoring has the potential to improve healthcare outcomes, the actual impact of these systems on patient health needs to be evaluated. Future research can focus on conducting clinical studies to evaluate the effectiveness of home environment health monitoring systems in improving healthcare outcomes.
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Ali, Z.; Hossain, M.S.; Muhammad, G.; Sangaiah, A.K. An intelligent healthcare system for detection and classification to discriminate vocal fold disorders. Future Gener. Comput. Syst. 2018, 85, 19–28. [Google Scholar] [CrossRef]
- Yang, G.; Xie, L.; Mäntysalo, M.; Zhou, X.; Pang, Z.; Da Xu, L.; Kao-Walter, S.; Chen, Q.; Zheng, L.R. A health-IoT platform based on the integration of intelligent packaging, unobtrusive bio-sensor, and intelligent medicine box. IEEE Trans. Ind. Inform. 2014, 10, 2180–2191. [Google Scholar] [CrossRef]
- Mohammed, K.; Zaidan, A.; Zaidan, B.; Albahri, O.S.; Alsalem, M.; Albahri, A.S.; Hadi, A.; Hashim, M. Real-time remote-health monitoring systems: A review on patients prioritisation for multiple-chronic diseases, taxonomy analysis, concerns and solution procedure. J. Med. Syst. 2019, 43, 1–21. [Google Scholar] [CrossRef] [PubMed]
- Chuah, M.C.; Fu, F. ECG anomaly detection via time series analysis. In Proceedings of the Frontiers of High Performance Computing and Networking ISPA 2007 Workshops: ISPA 2007 International Workshops SSDSN, UPWN, WISH, SGC, ParDMCom, HiPCoMB, and IST-AWSN Niagara Falls, Canada, 28 August–1 September 2007 Proceedings 5; Springer: Berlin/Heidelberg, Germany, 2007; pp. 123–135. [Google Scholar]
- Shahid, M.F.; Khanzada, T.J.S.; Tanveer, M.H. Integrating IoT and Deep Learning—The Driving Force of Industry 4.0. In A Roadmap for Enabling Industry 4.0 by Artificial Intelligence; John Wiley & Sons: Hoboken, NJ, USA, 2022; pp. 57–78. [Google Scholar]
- Shahidul Islam, M.; Islam, M.T.; Almutairi, A.F.; Beng, G.K.; Misran, N.; Amin, N. Monitoring of the human body signal through the Internet of Things (IoT) based LoRa wireless network system. Appl. Sci. 2019, 9, 1884. [Google Scholar] [CrossRef]
- Poongodi, T.; Krishnamurthi, R.; Indrakumari, R.; Suresh, P.; Balusamy, B. Wearable devices and IoT. In A Handbook of Internet of Things in Biomedical and Cyber Physical System; Springer: Berlin/Heidelberg, Germany, 2020; pp. 245–273. [Google Scholar]
- Ashfaq, Z.; Rafay, A.; Mumtaz, R.; Zaidi, S.M.H.; Saleem, H.; Zaidi, S.A.R.; Mumtaz, S.; Haque, A. A review of enabling technologies for Internet of Medical Things (IoMT) Ecosystem. Ain Shams Eng. J. 2022, 13, 101660. [Google Scholar] [CrossRef]
- Razzak, M.I.; Naz, S.; Zaib, A. Deep learning for medical image processing: Overview, challenges and the future. In Classification in BioApps: Automation of Decision Making; Springer: Berlin/Heidelberg, Germany, 2018; pp. 323–350. [Google Scholar]
- Li, J.; Jin, K.; Zhou, D.; Kubota, N.; Ju, Z. Attention mechanism-based CNN for facial expression recognition. Neurocomputing 2020, 411, 340–350. [Google Scholar] [CrossRef]
- Brauwers, G.; Frasincar, F. A general survey on attention mechanisms in deep learning. IEEE Trans. Knowl. Data Eng. 2021, 35, 3279–3298. [Google Scholar] [CrossRef]
- Alnaim, A.K.; Alwakeel, A.M. Machine-Learning-Based IoT–Edge Computing Healthcare Solutions. Electronics 2023, 12, 1027. [Google Scholar] [CrossRef]
- Ali, O.; Abdelbaki, W.; Shrestha, A.; Elbasi, E.; Alryalat, M.A.A.; Dwivedi, Y.K. A systematic literature review of artificial intelligence in the healthcare sector: Benefits, challenges, methodologies, and functionalities. J. Innov. Knowl. 2023, 8, 100333. [Google Scholar] [CrossRef]
- Ghosh, A.M.; Halder, D.; Hossain, S.A. Remote health monitoring system through IoT. In Proceedings of the 2016 5th International Conference on Informatics, Electronics and Vision (ICIEV), Dhaka, Bangladesh, 13–14 May 2016; pp. 921–926. [Google Scholar]
- Valsalan, P.; Baomar, T.A.B.; Baabood, A.H.O. IoT based health monitoring system. J. Crit. Rev. 2020, 7, 739–743. [Google Scholar]
- Durán-Vega, L.A.; Santana-Mancilla, P.C.; Buenrostro-Mariscal, R.; Contreras-Castillo, J.; Anido-Rifón, L.E.; García-Ruiz, M.A.; Montesinos-López, O.A.; Estrada-González, F. An IoT system for remote health monitoring in elderly adults through a wearable device and mobile application. Geriatrics 2019, 4, 34. [Google Scholar] [CrossRef]
- Hamim, M.; Paul, S.; Hoque, S.I.; Rahman, M.N.; Baqee, I.A. IoT based remote health monitoring system for patients and elderly people. In Proceedings of the 2019 International Conference on Robotics, Electrical and Signal Processing Techniques (ICREST), Dhaka, Bangladesh, 10–12 January 2019; pp. 533–538. [Google Scholar]
- Ali, A.; Almaiah, M.A.; Hajjej, F.; Pasha, M.F.; Fang, O.H.; Khan, R.; Teo, J.; Zakarya, M. An industrial IoT-based blockchain-enabled secure searchable encryption approach for healthcare systems using neural network. Sensors 2022, 22, 572. [Google Scholar] [CrossRef]
- Almaiah, M.A.; Hajjej, F.; Ali, A.; Pasha, M.F.; Almomani, O. A novel hybrid trustworthy decentralized authentication and data preservation model for digital healthcare IoT based CPS. Sensors 2022, 22, 1448. [Google Scholar] [CrossRef]
- Sujith, A.; Sajja, G.S.; Mahalakshmi, V.; Nuhmani, S.; Prasanalakshmi, B. Systematic review of smart health monitoring using deep learning and Artificial intelligence. Neurosci. Inform. 2022, 2, 100028. [Google Scholar] [CrossRef]
- Ghayvat, H.; Pandya, S.; Patel, A. Deep learning model for acoustics signal based preventive healthcare monitoring and activity of daily living. In Proceedings of the 2nd International Conference on Data, Engineering and Applications (IDEA), Bhopal, India, 28–29 February 2020; pp. 1–7. [Google Scholar]
- Cimtay, Y.; Ekmekcioglu, E. Investigating the use of pretrained convolutional neural network on cross-subject and cross-dataset EEG emotion recognition. Sensors 2020, 20, 2034. [Google Scholar] [CrossRef]
- Maier, M.; Elsner, D.; Marouane, C.; Zehnle, M.; Fuchs, C. DeepFlow: Detecting Optimal User Experience From Physiological Data Using Deep Neural Networks. In Proceedings of the International Conference on Autonomous Agents and Multiagent Systems, Montreal, QC, Canada, 13–17 May 2019; pp. 2108–2110. [Google Scholar]
- Pacheco, A.G.; Krohling, R.A. An attention-based mechanism to combine images and metadata in deep learning models applied to skin cancer classification. IEEE J. Biomed. Health Inform. 2021, 25, 3554–3563. [Google Scholar] [CrossRef]
- Rghioui, A.; Lloret, J.; Sendra, S.; Oumnad, A. A smart architecture for diabetic patient monitoring using machine learning algorithms. Healthcare 2020, 8, 348. [Google Scholar] [CrossRef]
- Aldahiri, A.; Alrashed, B.; Hussain, W. Trends in using IoT with machine learning in health prediction system. Forecasting 2021, 3, 181–206. [Google Scholar] [CrossRef]
- Tiwari, S.; Jain, A.; Sapra, V.; Koundal, D.; Alenezi, F.; Polat, K.; Alhudhaif, A.; Nour, M. A smart decision support system to diagnose arrhythymia using ensembled ConvNet and ConvNet-LSTM model. Expert Syst. Appl. 2023, 213, 118933. [Google Scholar] [CrossRef]
- Ed-Driouch, C.; Mars, F.; Gourraud, P.A.; Dumas, C. Addressing the Challenges and Barriers to the Integration of Machine Learning into Clinical Practice: An Innovative Method to Hybrid Human–Machine Intelligence. Sensors 2022, 22, 8313. [Google Scholar] [CrossRef]
- Botros, J.; Mourad-Chehade, F.; Laplanche, D. CNN and SVM-Based Models for the Detection of Heart Failure Using Electrocardiogram Signals. Sensors 2022, 22, 9190. [Google Scholar] [CrossRef] [PubMed]
- Chandrasekhar, N.; Peddakrishna, S. Enhancing Heart Disease Prediction Accuracy through Machine Learning Techniques and Optimization. Processes 2023, 11, 1210. [Google Scholar] [CrossRef]
- Mirjalali, S.; Peng, S.; Fang, Z.; Wang, C.H.; Wu, S. Wearable Sensors for Remote Health Monitoring: Potential Applications for Early Diagnosis of COVID-19. Adv. Mater. Technol. 2022, 7, 2100545. [Google Scholar] [CrossRef] [PubMed]
- Hammad, M.; Abd El-Latif, A.A.; Hussain, A.; Abd El-Samie, F.E.; Gupta, B.B.; Ugail, H.; Sedik, A. Deep learning models for arrhythmia detection in IoT healthcare applications. Comput. Electr. Eng. 2022, 100, 108011. [Google Scholar] [CrossRef]
- Nancy, A.A.; Ravindran, D.; Raj Vincent, P.D.; Srinivasan, K.; Gutierrez Reina, D. Iot-cloud-based smart healthcare monitoring system for heart disease prediction via deep learning. Electronics 2022, 11, 2292. [Google Scholar] [CrossRef]
- Haq, A.u.; Li, J.P.; Khan, S.; Alshara, M.A.; Alotaibi, R.M.; Mawuli, C. DACBT: Deep learning approach for classification of brain tumors using MRI data in IoT healthcare environment. Sci. Rep. 2022, 12, 15331. [Google Scholar] [CrossRef]
- Tsuneki, M. Deep learning models in medical image analysis. J. Oral Biosci. 2022, 64, 312–320. [Google Scholar] [CrossRef]
- Liu, T.; Siegel, E.; Shen, D. Deep learning and medical image analysis for COVID-19 diagnosis and prediction. Annu. Rev. Biomed. Eng. 2022, 24, 179–201. [Google Scholar] [CrossRef]
- Rincon, J.A.; Guerra-Ojeda, S.; Carrascosa, C.; Julian, V. An IoT and fog computing-based monitoring system for cardiovascular patients with automatic ECG classification using deep neural networks. Sensors 2020, 20, 7353. [Google Scholar] [CrossRef]
- Loey, M.; Manogaran, G.; Khalifa, N.E.M. A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images. Neural Comput. Appl. 2020, 1–13. [Google Scholar] [CrossRef]
- Moody, G.B.; Mark, R.G. The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. Mag. 2001, 20, 45–50. [Google Scholar] [CrossRef]
- Chen, C.; Hua, Z.; Zhang, R.; Liu, G.; Wen, W. Automated arrhythmia classification based on a combination network of CNN and LSTM. Biomed. Signal Process. Control 2020, 57, 101819. [Google Scholar] [CrossRef]
- Yıldırım, Ö.; Pławiak, P.; Tan, R.S.; Acharya, U.R. Arrhythmia detection using deep convolutional neural network with long duration ECG signals. Comput. Biol. Med. 2018, 102, 411–420. [Google Scholar] [CrossRef]
- Pławiak, P. Novel genetic ensembles of classifiers applied to myocardium dysfunction recognition based on ECG signals. Swarm Evol. Comput. 2018, 39, 192–208. [Google Scholar] [CrossRef]
- Hammad, M.; Iliyasu, A.M.; Subasi, A.; Ho, E.S.; Abd El-Latif, A.A. A multitier deep learning model for arrhythmia detection. IEEE Trans. Instrum. Meas. 2020, 70, 1–9. [Google Scholar] [CrossRef]
- Kim, Y.K.; Lee, M.; Song, H.S.; Lee, S.W. Automatic cardiac arrhythmia classification using residual network combined with long short-term memory. IEEE Trans. Instrum. Meas. 2022, 71, 1–17. [Google Scholar] [CrossRef]
- Acharya, U.R.; Fujita, H.; Lih, O.S.; Hagiwara, Y.; Tan, J.H.; Adam, M. Automated detection of arrhythmias using different intervals of tachycardia ECG segments with convolutional neural network. Inf. Sci. 2017, 405, 81–90. [Google Scholar] [CrossRef]
- Yao, Q.; Wang, R.; Fan, X.; Liu, J.; Li, Y. Multi-class arrhythmia detection from 12-lead varied-length ECG using attention-based time-incremental convolutional neural network. Inf. Fusion 2020, 53, 174–182. [Google Scholar] [CrossRef]
- Zhou, S.; Tan, B. Electrocardiogram soft computing using hybrid deep learning CNN-ELM. Appl. Soft Comput. 2020, 86, 105778. [Google Scholar] [CrossRef]
Component | Specifications |
---|---|
NodeMCU | Open-source firmware and development board based on the ESP8266 Wi-Fi module. It has a USB interface, voltage regulator for stable power supply, 11 digital I/O pins, 1 analog input pin, and 1 UART communication interface. Compatible with Arduino IDE. |
MAX30100 | High-sensitivity pulse oximeter and heart rate sensor module. Measures SpO2 and HR using integrated LEDs, photodetectors, and low-noise electronics. Includes ambient light cancellation algorithm for improved accuracy. |
AD8232 ECG sensor | Single-lead ECG sensor with low power consumption and small form factor. Includes an instrumentation amplifier, right-leg drive amplifier, lead-off detection circuit, and comparator. Measures ECG signals accurately and can detect arrhythmias and heart rate. |
MLX90614 Temperature Sensor | Non-contact infrared temperature sensor with a wide measurement range of −70 °C to 380 °C. Uses an infrared-sensitive thermopile detector for temperature measurement. Provides calibrated digital output for ambient and object temperatures over an I2C interface. |
Parameter | Value 1 | Value 2 | Value 3 | Value Used |
---|---|---|---|---|
Number of filters | 32, 64, 128 | 64, 128, 256 | 128, 256, 512 | 64, 128, 256 |
Neuron size | 128, 256 | 256, 512 | 512, 256 | 512, 256 |
Dropout rate | 0.2 | 0.5 | 0.8 | 0.5 |
Learning rate | 0.1 | 0.01 | 0.001 | 0.001 |
Batch size | 128 | 256 | 512 | 512 |
Epochs | 25 | 40 | 50 | 25 |
Class | Precision | Recall | F1-Score | Support |
---|---|---|---|---|
Normal Beat | 0.98 | 1.00 | 0.99 | 18,118 |
Supraventricular premature beat | 0.90 | 0.78 | 0.83 | 556 |
Premature ventricular contraction | 0.97 | 0.93 | 0.95 | 1448 |
Fusion of ventricular | 0.88 | 0.50 | 0.64 | 162 |
Unclassifiable beat | 0.99 | 0.99 | 0.99 | 1608 |
accuracy | 0.98 | 21,892 | ||
macro avg | 0.94 | 0.84 | 0.88 | 21,892 |
weighted avg | 0.98 | 0.98 | 0.98 | 21,892 |
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Share and Cite
Islam, M.R.; Kabir, M.M.; Mridha, M.F.; Alfarhood, S.; Safran, M.; Che, D. Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time. Sensors 2023, 23, 5204. https://doi.org/10.3390/s23115204
Islam MR, Kabir MM, Mridha MF, Alfarhood S, Safran M, Che D. Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time. Sensors. 2023; 23(11):5204. https://doi.org/10.3390/s23115204
Chicago/Turabian StyleIslam, Md. Reazul, Md. Mohsin Kabir, Muhammad Firoz Mridha, Sultan Alfarhood, Mejdl Safran, and Dunren Che. 2023. "Deep Learning-Based IoT System for Remote Monitoring and Early Detection of Health Issues in Real-Time" Sensors 23, no. 11: 5204. https://doi.org/10.3390/s23115204